Anushka Yeole,
Kalyani Alisetty,
- Student Department of Management, Sinhgad Institute of Business Administration and Research, Pune Maharashtra India
- Professor MCA, Sinhgad Institute of Business Administration and Research, Pune Maharashtra India
Abstract
A comprehensive study of sentiment analysis, a vital task in the realms of natural language processing (NLP) and artificial intelligence (AI). Sentiment analysis involves the extraction and classification of subjective information from textual data, determining whether the sentiment expressed is positive or negative. This paper investigates different approaches and methodologies used in sentiment analysis, encompassing machine learning models as well. Additionally, it discusses the challenges faced in sentiment analysis, such as handling sarcasm, irony, and context-dependent sentiment. Furthermore, the paper highlights the intersection of sentiment analysis with other NLP tasks, such as text summarization using Bag of Words (BOW) and Term Frequency-Inverse Document Frequency (TFIDF) and emphasizes the importance of understanding human language nuances for accurate sentiment interpretation. This project leverages MLflow and Prefect to manage, track, and orchestrate sentiment analysis machine learning experiments, ensuring efficient workflow automation and comprehensive performance evaluation. The integration of MLflow and Prefect not only enhances the reproducibility of experiments but also facilitates the seamless deployment of sentiment analysis models into production environments. By addressing the evolving complexities in textual data interpretation, this project aims to advance the field of sentiment analysis, contributing to more nuanced and accurate sentiment detection systems. Through an examination of recent research trends and applications, this paper underscores the significance of sentiment analysis in diverse domains ranging from social media monitoring to market research.
Keywords: Text Summarization, Sentiment Analysis, NLP, AI, Human Language, Feature Extraction Techniques, Data Science Life Cycle.
[This article belongs to Journal of Software Engineering Tools & Technology Trends(josettt)]
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Journal of Software Engineering Tools & Technology Trends
| Volume | 11 |
| Issue | 02 |
| Received | June 4, 2024 |
| Accepted | July 11, 2024 |
| Published | July 22, 2024 |